05 July, 2017

Background

  • Wind turbine modelling provide key opportunity for decarbonisation

  • Cost competitive against many conventional technologies

  • Multiple actors

  • Parameters

Wind Turbine Site Selection

  • Difficulties in identifying suitable sites
  • Different actors consider different parameters:

Site developers:

- Largely concerned about **Economic** parameters
- Sites endowment
- Easy to understand

Local parties:

- **Social** and **Environmental** concerns
- Visual intrusion of sites
- Difficult to assess

Wind Turbine Site Selection

Existing Research

Existing Research

  • Extensive use of Geospatial Information Systems (GIS) to identify suitable sites
  • Multi-criteria decision analysis (MCDA) used extensively to determine best options
  • Use economic, environmental and social parameters

Example Studies

[1] J. R. Janke, "Multicriteria GIS modeling of wind and solar farms in Colorado," Renew. Energy, vol. 35, pp. 2228-2234, 2010.

[2] J. J. W. Watson and M. D. Hudson, "Regional Scale wind farm and solar farm suitability assessment using GIS-assisted multi-criteria evaluation," Landsc. Urban Plan., vol. 138, pp. 20-31, 2015.

Challenges

Research Gap

  • Are we using the right parameters to assess site suitability?
  • Can we accurately predict the likelihood success rate based on geospatial parameters?

Research

Research Approach

  • Inverse/Retrospective GIS approach used to develop model parameters
  • Logistic regression analysis used to assess influence of parameters

Data Collection

Location of Wind Turbines used within the analysis

Location of Wind Turbines used within the analysis

Model Parameters

Key Results

  • Optimisation of model to identify key parameters

Results

Variable Full Reduced Nested: England Nested: Scotland Nested: Wales
Observations 1476 1476 646 698 132
Parameters 27 9 10 11 10
Nagelkirke R2 0.11 0.1 0.11 0.16 0.24
Pearson Chi-squared 113.7 111.9 51.5 89.3 25.9
Residual deviance 1932 1932 833 895 157
Model Accuracy 60% 62% 60% 65% 57%
  • Relatively poor overall model fit

Model Generalisation

Use the results from the analysis

Outcomes

  • Range of siginficant parameters identified
  • Quantitiatvely connected social characteristics to acceptance rates.

Future Work

  • Build these results into decision making analysis.
  • Further modelling: inclusion of more parameters
  • Assess national differences
  • Can we understand where sites are best located?

Thank You

Appendix

Full Table of Parameters